class_docstr: Base class for all leapfrog integrators.
  
  [Leapfrog integrators](https://en.wikipedia.org/wiki/Leapfrog_integration)
  numerically integrate differential equations of the form:
  
  ```none
  v' = dv/dt = F(x)
  x' = dx/dt = v
  ```
  
  This class defines minimal requirements for leapfrog integration calculations.
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docstr: Computes the integration.
  
  Args:
    momentum_parts: Python `list` of `Tensor`s representing momentum for each
      state part.
    state_parts: Python `list` of `Tensor`s which collectively representing
      the state.
    target: Batch of scalar `Tensor` representing the target (i.e.,
      unnormalized log prob) evaluated at `state_parts`.
    target_grad_parts: Python `list` of `Tensor`s representing the gradient of
      `target` with respect to each of `state_parts`.
    kinetic_energy_fn: Python callable that can evaluate the kinetic energy
      of the given momentum.
    name: Python `str` used to group ops created by this function.
  
  Returns:
    next_momentum_parts: Python `list` of `Tensor`s representing new momentum.
    next_state_parts: Python `list` of `Tensor`s which collectively
      representing the new state.
    next_target: Batch of scalar `Tensor` representing the target (i.e.,
      unnormalized log prob) evaluated at `next_state_parts`.
    next_target_grad_parts: Python `list` of `Tensor`s representing the
      gradient of `next_target` with respect to each of `next_state_parts`.
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code: def __call__(
      self,
      momentum_parts,
      state_parts,
      target=None,
      target_grad_parts=None,
      kinetic_energy_fn=None,
      name=None,
  ):
      """Computes the integration.
  
      Args:
        momentum_parts: Python `list` of `Tensor`s representing momentum for each
          state part.
        state_parts: Python `list` of `Tensor`s which collectively representing
          the state.
        target: Batch of scalar `Tensor` representing the target (i.e.,
          unnormalized log prob) evaluated at `state_parts`.
        target_grad_parts: Python `list` of `Tensor`s representing the gradient of
          `target` with respect to each of `state_parts`.
        kinetic_energy_fn: Python callable that can evaluate the kinetic energy
          of the given momentum.
        name: Python `str` used to group ops created by this function.
  
      Returns:
        next_momentum_parts: Python `list` of `Tensor`s representing new momentum.
        next_state_parts: Python `list` of `Tensor`s which collectively
          representing the new state.
        next_target: Batch of scalar `Tensor` representing the target (i.e.,
          unnormalized log prob) evaluated at `next_state_parts`.
        next_target_grad_parts: Python `list` of `Tensor`s representing the
          gradient of `next_target` with respect to each of `next_state_parts`.
      """
      raise NotImplementedError("Integrate logic not implemented.")
  
file_path: probability/tensorflow_probability/python/mcmc/internal/leapfrog_integrator.py
imports: import abc
  import six
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 1 for calling the LeapfrogIntegrator obj's underlying member method code with example usage: obj.__call__(momentum_parts, state_parts, target, target_grad_parts, kinetic_energy_fn, name) without return types